Attention, as a critical ability of the human system, enables people to focus on more useful information when processing multi-modal information. Originated from the area of neural machine translation for modeling the contextual information, attention mechanism in machine vision has improved the success of various applications in recent years. It continues to be an omnipresent component in state-of-the-art models, such as image classification [1]}, image captioning [2]}, and scene segmentation [3]}. In visual tracking, CNN-based methods with attention mechanisms can integrate different visual information to improve tracking accuracy. J. Choi et al. [4]} proposed an attention network to switch among different features to select the suitable tracking mode. Z. Zhu et al. [5]} incorporated optical flow based on deep learning into the tracking pipeline. Other tracking methods [6]}, [7]}, [8]}, [9]} used feature maps extracted from deep neural networks to select the appropriate tracking mode for better performance. However, these methods are not suitable for the complex UAV tracking scenarios, e.g., partial occlusion, clutter background, and viewpoint changes.
